|Cetin, A. enis|
Submitted to: Biological Engineering (ASABE)
Publication Type: Peer reviewed journal
Publication Acceptance Date: 11/1/2007
Publication Date: 4/1/2008
Publication URL: naldc.nal.usda.gov/download/49531/PDF
Citation: Ince, N.F., Onaran, I., Pearson, T.C., Tewfik, A., Cetin, A. 2008. Discrimination Between Closed and Open Shell (Turkish) Pistachio Nuts Using Undecimated Wavelet Packet Transform. Biological Engineering (ASABE). 1(2):159-172. Interpretive Summary: Current methods for separating closed- from open-shell pistachio nuts have low accuracy, high cost, and can damage the nuts. A new method for separating closed- and open-shell pistachio nuts uses the sound emanating from the nuts as they drop onto a plate as the basis for discrimination. This study expanded the capability of the original acoustic based sorter by improving accuracy and making the system more adaptable to other commodities. Accuracy is improved by over ten percent for a variety of nuts that is particularly difficult to separate. This can represent a saving of millions of dollars a year for pistachio processors. Additionally, some nuts that are difficult to separate need to be separated by hand, increasing the possibility of introducing food pathogens. This work will help reduce this risk.
Technical Abstract: Due to low consumer acceptance and the possibility of immature kernels, closed-shell pistachio nuts should be separated from open-shell nuts before reaching the consumer. The feasibility of a system using impact acoustics as a means of classifying closed-shell nuts from open-shell nuts has already been shown to have better discrimination performance than a mechanical system. The accuracy of an impact acoustics based system is determined by the signal processing and feature extraction procedures. In this paper, a new time-frequency plain feature extraction and classification algorithm was developed to discriminate between open- and closed-shell pistachio nuts produced in the Gaziantep region of Turkey. The proposed approach relies on the analysis of the impact acoustics signal of pistachio nuts, which are emitted from the nuts after impacting with a steel plate after dropping from a certain height. Features are extracted by decomposing the acoustic signals into time and frequency components, using double tree undecimated wavelet packet transform. The most discriminative features from the dual tree nodes are selected by a wrapper strategy that includes the structural pruning of the double-tree feature dictionary. The proposed approach requires no prior knowledge of the relevant time or frequency content of the acoustic signals. The algorithm used a small number of features and achieved a classification accuracy of 91.7% on the validation data set, while separating the closed shells from open ones. A previously implemented algorithm, which uses maximum signal amplitude, absolute integration, and gradient features, achieved 82% classification accuracy on the same dataset. The results show that the time-frequency features extracted from impact acoustics can be used successfully for classification of open- and closed-shell Turkish pistachios.